In current cellular networks, centralized synchronization protocols are widely used to establish and maintain coordination among the network nodes. However, centralized methods may be sensitive to congestion problems and failures of central (fusion) stations. Thus, centralized techniques may not be considered robust and may be considered inefficient in complex networks, at least with respect to scaling, changes in topology and mobility. In contrast, distributed synchronization and self-synchronization, well known phenomena in biological and physical systems, have recently attracted growing attention in engineering.
Reference with regard to dynamic systems and/or synchronization may be made to the following:    Hoppensteadt F. C. and Izhikevich E. M., “Weakly Connected Neural Networks”. Springer-Verlag, New York, 1997.    Strogatz S., “Sync: The Emerging Science of Spontaneous Order”, NY: Hyperion 2003.    Acebron J. et al, “The Kuramoto model: A simple paradigm for synchronization phenomena”, Reviews of Modem Physics, v. 77, pp. 137-185, January 2005.    S. Barbarossa, G. Scutari, “Decentralized Maximum-Likelihood Estimation for Sensor Networks Composed of Nonlineary Coupled Dynamical Systems”, IEEE Trans. on Signal Processing, pp. 3456-3470, v. 55, No. 7, July 2007.    S. Barbarossa, G. Scutari, “Bio-Inspired Sensor Network Design”, IEEE Signal Processing Magazine, pp. 26-35, May 2007.    Mirollo R. E, Strogatz S. H., “Synchronization of pulse-coupled biological oscillators”, SIAM J. Appl. Math, v. 50, pp. 1645-1662, No. 6, December 1990.    Hong Y.-W., Scaglione A., “A Scalable Synchronzation Protocol for Large Scale Sensor Networks and its Applications”, IEEE JSAC, v. 23, pp. 1085-1099, No. 5, May 2005.    Kuramato Y., Lec. Notes in Physics No. 30, Springer N.Y. 1975.    Haykin, S., “Cognitive Radio: Brain-Empowered Wireless Communications”, IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, February 2005.
Future development of wireless communication systems assumes co-existence of various communication systems with dynamical frequency allocation/access without centralized control. It gives rise to the concept of cognitive radio networks. One definition for cognitive radio is given by Haykin as follows: “The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: highly reliable communication whenever and wherever needed; efficient utilization of the radio spectrum.” See Haykin, S., “Cognitive Radio: Brain-Empowered Wireless Communications”, IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, February 2005.